HighlightsMachine learning can incorporate a variety of data from low-cost sensors and estimate actual ET by comparison with short-term, higher-cost measurements.On-farm weather monitoring can be leveraged to estimate site-specific crop-water requirements.Expanding spatial coverage of weather and actual ET through on-farm monitoring will facilitate localization and leverage publicly available weather data to guide irrigation decisions and improve irrigation water management.Abstract. One of the basic challenges to adopting science-based irrigation scheduling is providing reliable, site-specific estimates of actual crop water demand. While agro-meteorology networks cover most agricultural production areas in the U.S., widely spaced stations represent regionally specific, rather than site-specific, conditions. A variety of low to moderate cost commercial weather stations are available but do not provide directly useful information, such as actual evapotranspiration (ETa), or the ability to incorporate additional sensors. We demonstrate that machine learning methods can provide real-time, site-specific information about ETa and crop water demand using on-farm sensors and public weather information. Two years of field experiments were conducted at four irrigated field sites with crops including snap beans, alfalfa, and pasture. On-farm data were compared to publicly available data originating at nearby agro-meteorology network stations. The machine learning procedure can robustly estimate ETa using data from a few basic sensors, but the resulting estimate is sensitive to the range of conditions that are used as training data. The results demonstrate that machine learning can be used with affordable sensors and publicly available data to improve local estimates of crop water demand when high-quality measurements can be co-located for short periods of time. Supplementary sensors can also be integrated into a tailored monitoring plan to estimate crop stress and other operational considerations. Keywords: Agro-meteorology, Irrigation requirement, Machine learning, Site-specific Irrigation.

This study reviews how mini-lysimeters have been used effectively to optimize irrigation control in container horticulture production. Lysimeters are devices that measure evapotranspiration (ET) from the water balance of a fixed soil volume. The primary components of lysimeter-controlled irrigation are load cell sensors, a multiplexer, a data logger, a controller, and solenoid valves. The two common mini-lysimeter systems are platform lysimeters and suspension lysimeters. In these systems, a bending-beam single-point load cell is fastened between two plates, and a container is placed directly on the top platform. Platform lysimeters are commonly used for smaller pot sizes, and suspension lysimeters have been used for large shade trees up to 2.8 m tall and weighing 225 kg. Mini-lysimeters have been used for decades to calibrate ET models and create on-demand irrigation control programs that replenish plant daily water use or maintain deficit conditions. Research has demonstrated that lysimeter-based irrigation can respond more effectively to seasonal and diurnal variations in water demand, increasing irrigation cycles when evaporative demand is high, and decreasing irrigation cycles when demand is low. A strength of these systems is that for containerized plants, such as nursery production systems, mini-lysimeters capture whole-plant water use, which presents a more holistic measure compared with soil moisture sensors or leaf moisture sensors.

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